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Key Responsibilities and Required Skills for Data & Database Roles

💰 $ - $

Data EngineeringDatabase AdministrationAnalyticsSQLCloud

🎯 Role Definition

Data & Database Roles encompass a range of positions responsible for designing, building, operating, optimizing and securing the systems that store and serve enterprise data. Typical responsibilities include schema and data model design, SQL and query performance optimization, ETL pipeline development, cloud database administration, backup and recovery planning, data governance, monitoring and incident response, and collaboration with analytics, engineering and product teams to ensure data availability, integrity and scalability.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst / Reporting Analyst
  • Junior Database Administrator / Database Developer
  • Software Engineer with SQL or data experience

Advancement To:

  • Senior Database Administrator / Senior Data Engineer
  • Data Architect / Data Platform Engineer
  • Lead Data Engineer or Manager, Data Engineering

Lateral Moves:

  • Business Intelligence (BI) Developer / Analytics Engineer
  • Data Scientist (with strong engineering focus)
  • DevOps/SRE with database specialization

Core Responsibilities

Primary Functions

  • Design, implement and maintain scalable relational and NoSQL database solutions (PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, Cassandra) to support both OLTP and OLAP workloads, ensuring high availability and fault tolerance in production environments.
  • Write, review and optimize complex SQL queries, stored procedures, triggers and views to reduce latency, lower resource consumption and improve throughput for transactional and analytical systems.
  • Architect and maintain ETL/ELT pipelines (Airflow, dbt, Talend, Informatica, Azure Data Factory) to reliably ingest, transform and deliver data to data warehouses and downstream consumers while ensuring data quality and lineage.
  • Lead performance tuning and capacity planning initiatives, including index strategy, partitioning, query plan analysis, and database configuration tuning to meet SLAs for concurrency, latency and nightly batch windows.
  • Design and execute database schema and data model changes with careful migration plans, schema evolution strategies and backward compatibility considerations to minimize downtime and data loss risk.
  • Administer backup, restore and disaster recovery strategies (point-in-time recovery, log shipping, replication, snapshots) and regularly validate recovery procedures through testing and runbooks.
  • Implement and manage replication, clustering and high-availability configurations (e.g., PostgreSQL streaming replication, MySQL Group Replication, Always On Availability Groups, MongoDB replica sets) to ensure resilience and read-scaling.
  • Lead database capacity forecasting and provisioning across on-premise and cloud environments (AWS RDS/Aurora, Azure SQL, Google Cloud SQL, Snowflake, Redshift), optimizing cost and performance tradeoffs.
  • Implement robust security controls: role-based access control (RBAC), encryption at rest and in transit, auditing, secrets management, and compliance with regulatory frameworks such as GDPR, HIPAA or SOC2.
  • Monitor database health, set up alerting and observability (Prometheus, Grafana, Datadog, New Relic), and act as the first responder to incidents impacting data availability or integrity.
  • Troubleshoot production incidents end-to-end, perform root cause analysis, document postmortems and implement corrective actions to eliminate recurrence and reduce mean time to recovery (MTTR).
  • Design and deliver data warehousing solutions and dimensional models (star and snowflake schemas) for reporting and analytics workloads using tools such as Snowflake, BigQuery or Redshift.
  • Develop automation and self-service tooling for database provisioning, schema migrations, deployments and configuration management (Terraform, Ansible, Chef) to enable repeatable and auditable operations.
  • Collaborate with data scientists, analysts and product teams to understand data needs, translate requirements into data model changes and provide reliable data access for analytics and ML pipelines.
  • Own data lifecycle management including archival, purging, retention policies and GDPR-compliant data deletion, balancing regulatory needs with business and technical constraints.
  • Create, maintain and evangelize best practices, standards and documentation for database development, schema design, query optimization and operational runbooks to scale team knowledge.
  • Implement observability for data quality: automated tests, anomaly detection, sampling and reconciliation jobs to ensure accuracy and trustworthiness of production datasets.
  • Lead migration projects to modern data platforms (lift-and-shift to cloud, migration from monolithic RDBMS to cloud-native warehouses), planning cutovers, rollback strategies and performance validation.
  • Mentor and coach junior DBAs/engineers, conduct code and schema reviews, and help build a culture of reliability, automation and proactive monitoring across the data organization.
  • Partner with security and compliance teams to perform database risk assessments, remediate vulnerabilities, and support audits and evidence collection for regulatory compliance.
  • Optimize storage, caching and indexing strategies (materialized views, Redis, Memcached) to accelerate read-heavy APIs and reporting layers while controlling infrastructure costs.
  • Define and track key performance indicators (KPIs) for data platform health, SLA attainment, deployment frequency, incident counts and mean time to resolution, and drive continuous improvement.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Produce clear runbooks, on-call rotation documentation and handover notes for peak-traffic and maintenance windows.
  • Evaluate and pilot emerging data technologies (catalogs, lakehouses, streaming engines) and recommend adoption when aligned with business value and operational readiness.
  • Coordinate with vendors and managed service providers for licensing, support escalations and managed database offerings.
  • Assist in budgeting and cost allocation for database infrastructure, right-sizing instances and optimizing storage tiers.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL expertise (query tuning, window functions, CTEs, execution plans) across at least one major RDBMS (Postgres, MySQL, SQL Server, Oracle).
  • Experience administering production relational databases (installation, upgrades, patching, replication, failover).
  • Hands-on knowledge of cloud-managed databases and analytics platforms: AWS RDS/Aurora, Redshift, Snowflake, Google BigQuery, Azure SQL.
  • Proficiency with NoSQL systems and use-cases (MongoDB, Cassandra, DynamoDB) including data modeling and performance considerations.
  • Data modeling and dimensional design for data warehouses and BI use cases (star schema, slowly changing dimensions).
  • ETL/ELT design and orchestration experience (Airflow, dbt, Apache NiFi, Azure Data Factory).
  • Database performance tuning: indexing strategies, partitioning, query plan analysis, concurrency controls and lock management.
  • Backup and disaster recovery planning and implementation, including point-in-time recovery and replication-based failover strategies.
  • Familiarity with data governance, data lineage, metadata management and tools like Apache Atlas, Amundsen or Collibra.
  • Scripting and automation skills in Python, Bash, or PowerShell for operational tasks, monitoring and data validation.
  • Infrastructure-as-Code and configuration management experience (Terraform, CloudFormation, Ansible) for database provisioning.
  • Familiar with containerization and orchestration patterns for databases and related services (Docker, Kubernetes operators).
  • Monitoring and observability tooling: Prometheus, Grafana, Datadog, New Relic and log aggregation systems (ELK/EFK).
  • Understanding of security best practices for databases: encryption, access controls, secrets management and auditing.
  • Knowledge of streaming and real-time data platforms (Kafka, Kinesis, Pulsar) and their integration with storage layers.

Soft Skills

  • Strong verbal and written communication skills for cross-functional collaboration and technical documentation.
  • Stakeholder management and the ability to translate business requirements into data solutions.
  • Analytical problem-solving and meticulous attention to detail when debugging production issues.
  • Prioritization and time management to balance feature delivery, operational reliability and technical debt.
  • Coaching and mentoring mindset to develop junior engineers and disseminate best practices.
  • Customer-focused approach with a sense of ownership for data availability and user experience.
  • Collaboration in agile teams: participates in sprint planning, retrospectives and iterative delivery.
  • Clear decision-making under pressure during incident response and production outages.
  • Change management discipline and ability to drive safe rollouts and migrations.
  • Continuous learning orientation with curiosity about new database technologies and cloud services.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, Mathematics, Statistics or related field, or equivalent practical experience in database engineering/administration.

Preferred Education:

  • Master's degree in Data Science, Computer Science, Information Systems, or MBA with technical emphasis; professional certifications (AWS Certified Database Specialty, Google Cloud Professional Data Engineer, Oracle Certified Professional) are a plus.

Relevant Fields of Study:

  • Computer Science
  • Information Systems
  • Data Science
  • Software Engineering
  • Mathematics / Statistics

Experience Requirements

Typical Experience Range:

  • 2–8 years for mid-level roles; 5–12+ years for Senior/Lead/Architect positions depending on scope and scale.

Preferred:

  • Demonstrated experience operating production databases at scale (high throughput, high availability) in cloud or hybrid environments.
  • Proven track record of leading migrations to cloud-native data platforms, optimizing query performance and delivering data platform reliability improvements.
  • Prior experience working with cross-functional product, analytics and security teams and delivering measurable improvements in data quality and system uptime.
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